{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:90% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## notebookの表示領域広げる\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:90% !important; }</style>\"))\n",
    "\n",
    "## 描画できるようにする＆綺麗に描画できるようにする\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "%config InlineBackend.figure_formats = {'png', 'retina'}\n",
    "\n",
    "## 自動で時間表示する\n",
    "%load_ext autotime\n",
    "\n",
    "## ライブラリのリロードを行えるようにする\n",
    "%load_ext autoreload\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "## dataframeの表示を広げる\n",
    "def disp_full(x, drows=False, dcols=True):\n",
    "    if drows:\n",
    "        pd.set_option('display.max_rows', x.shape[0])\n",
    "    if dcols:\n",
    "        pd.set_option('display.max_columns', x.shape[1])\n",
    "    display(x)\n",
    "    pd.reset_option('display.max_rows')\n",
    "    pd.reset_option('display.max_columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 766 µs\n"
     ]
    }
   ],
   "source": [
    "import os, sys, gc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 385 ms\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 7.84 ms\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm_notebook as tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 298 ms\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 21.7 ms\n"
     ]
    }
   ],
   "source": [
    "import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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RegExp(\"^(data|aria)-[\"+r.ATTRIBUTE_NAME_CHAR+\"]*$\")),Properties:{accept:0,acceptCharset:0,accessKey:0,action:0,allowFullScreen:o,allowTransparency:0,alt:0,as:0,async:o,autoComplete:0,autoPlay:o,capture:o,cellPadding:0,cellSpacing:0,charSet:0,challenge:0,checked:i|o,cite:0,classID:0,className:0,cols:u,colSpan:0,content:0,contentEditable:0,contextMenu:0,controls:o,controlsList:0,coords:0,crossOrigin:0,data:0,dateTime:0,default:o,defer:o,dir:0,disabled:o,download:c,draggable:0,encType:0,form:0,formAction:0,formEncType:0,formMethod:0,formNoValidate:o,formTarget:0,frameBorder:0,headers:0,height:0,hidden:o,high:0,href:0,hrefLang:0,htmlFor:0,httpEquiv:0,icon:0,id:0,inputMode:0,integrity:0,is:0,keyParams:0,keyType:0,kind:0,label:0,lang:0,list:0,loop:o,low:0,manifest:0,marginHeight:0,marginWidth:0,max:0,maxLength:0,media:0,mediaGroup:0,method:0,min:0,minLength:0,multiple:i|o,muted:i|o,name:0,nonce:0,noValidate:o,open:o,optimum:0,pattern:0,placeholder:0,playsInline:o,poster:0,preload:0,profile:0,radioGroup:0,readOnly:o,referrerPolicy:0,rel:0,required:o,reversed:o,role:0,rows:u,rowSpan:a,sandbox:0,scope:0,scoped:o,scrolling:0,seamless:o,selected:i|o,shape:0,size:u,sizes:0,span:u,spellCheck:0,src:0,srcDoc:0,srcLang:0,srcSet:0,start:a,step:0,style:0,summary:0,tabIndex:0,target:0,title:0,type:0,useMap:0,value:0,width:0,wmode:0,wrap:0,about:0,datatype:0,inlist:0,prefix:0,property:0,resource:0,typeof:0,vocab:0,autoCapitalize:0,autoCorrect:0,autoSave:0,color:0,itemProp:0,itemScope:o,itemType:0,itemID:0,itemRef:0,results:0,security:0,unselectable:0},DOMAttributeNames:{acceptCharset:\"accept-charset\",className:\"class\",htmlFor:\"for\",httpEquiv:\"http-equiv\"},DOMPropertyNames:{},DOMMutationMethods:{value:function(t,e){if(null==e)return t.removeAttribute(\"value\");\"number\"!==t.type||!1===t.hasAttribute(\"value\")?t.setAttribute(\"value\",\"\"+e):t.validity&&!t.validity.badInput&&t.ownerDocument.activeElement!==t&&t.setAttribute(\"value\",\"\"+e)}}};t.exports=s},function(t,e,n){\"use strict\";(function(e){function r(t,e,n,r){var i=void 0===t[n];null!=e&&i&&(t[n]=o(e,!0))}var i=n(24),o=n(174),a=(n(85),n(96)),u=n(177);n(2);void 0!==e&&e.env;var c={instantiateChildren:function(t,e,n,i){if(null==t)return null;var o={};return u(t,r,o),o},updateChildren:function(t,e,n,r,u,c,s,l,f){if(e||t){var p,h;for(p in e)if(e.hasOwnProperty(p)){h=t&&t[p];var d=h&&h._currentElement,v=e[p];if(null!=h&&a(d,v))i.receiveComponent(h,v,u,l),e[p]=h;else{h&&(r[p]=i.getHostNode(h),i.unmountComponent(h,!1));var g=o(v,!0);e[p]=g;var m=i.mountComponent(g,u,c,s,l,f);n.push(m)}}for(p in t)!t.hasOwnProperty(p)||e&&e.hasOwnProperty(p)||(h=t[p],r[p]=i.getHostNode(h),i.unmountComponent(h,!1))}},unmountChildren:function(t,e){for(var n in t)if(t.hasOwnProperty(n)){var r=t[n];i.unmountComponent(r,e)}}};t.exports=c}).call(e,n(156))},function(t,e,n){\"use strict\";var r=n(82),i=n(364),o={processChildrenUpdates:i.dangerouslyProcessChildrenUpdates,replaceNodeWithMarkup:r.dangerouslyReplaceNodeWithMarkup};t.exports=o},function(t,e,n){\"use strict\";function r(t){}function i(t){return!(!t.prototype||!t.prototype.isReactComponent)}function o(t){return!(!t.prototype||!t.prototype.isPureReactComponent)}var a=n(1),u=n(3),c=n(26),s=n(87),l=n(15),f=n(88),p=n(39),h=(n(9),n(168)),d=n(24),v=n(51),g=(n(0),n(81)),m=n(96),y=(n(2),{ImpureClass:0,PureClass:1,StatelessFunctional:2});r.prototype.render=function(){var t=p.get(this)._currentElement.type,e=t(this.props,this.context,this.updater);return e};var _=1,b={construct:function(t){this._currentElement=t,this._rootNodeID=0,this._compositeType=null,this._instance=null,this._hostParent=null,this._hostContainerInfo=null,this._updateBatchNumber=null,this._pendingElement=null,this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._renderedNodeType=null,this._renderedComponent=null,this._context=null,this._mountOrder=0,this._topLevelWrapper=null,this._pendingCallbacks=null,this._calledComponentWillUnmount=!1},mountComponent:function(t,e,n,u){this._context=u,this._mountOrder=_++,this._hostParent=e,this._hostContainerInfo=n;var s,l=this._currentElement.props,f=this._processContext(u),h=this._currentElement.type,d=t.getUpdateQueue(),g=i(h),m=this._constructComponent(g,l,f,d);g||null!=m&&null!=m.render?o(h)?this._compositeType=y.PureClass:this._compositeType=y.ImpureClass:(s=m,null===m||!1===m||c.isValidElement(m)||a(\"105\",h.displayName||h.name||\"Component\"),m=new r(h),this._compositeType=y.StatelessFunctional);m.props=l,m.context=f,m.refs=v,m.updater=d,this._instance=m,p.set(m,this);var b=m.state;void 0===b&&(m.state=b=null),(\"object\"!=typeof b||Array.isArray(b))&&a(\"106\",this.getName()||\"ReactCompositeComponent\"),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1;var x;return x=m.unstable_handleError?this.performInitialMountWithErrorHandling(s,e,n,t,u):this.performInitialMount(s,e,n,t,u),m.componentDidMount&&t.getReactMountReady().enqueue(m.componentDidMount,m),x},_constructComponent:function(t,e,n,r){return this._constructComponentWithoutOwner(t,e,n,r)},_constructComponentWithoutOwner:function(t,e,n,r){var i=this._currentElement.type;return t?new i(e,n,r):i(e,n,r)},performInitialMountWithErrorHandling:function(t,e,n,r,i){var o,a=r.checkpoint();try{o=this.performInitialMount(t,e,n,r,i)}catch(u){r.rollback(a),this._instance.unstable_handleError(u),this._pendingStateQueue&&(this._instance.state=this._processPendingState(this._instance.props,this._instance.context)),a=r.checkpoint(),this._renderedComponent.unmountComponent(!0),r.rollback(a),o=this.performInitialMount(t,e,n,r,i)}return o},performInitialMount:function(t,e,n,r,i){var o=this._instance,a=0;o.componentWillMount&&(o.componentWillMount(),this._pendingStateQueue&&(o.state=this._processPendingState(o.props,o.context))),void 0===t&&(t=this._renderValidatedComponent());var u=h.getType(t);this._renderedNodeType=u;var c=this._instantiateReactComponent(t,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,r,e,n,this._processChildContext(i),a);return s},getHostNode:function(){return d.getHostNode(this._renderedComponent)},unmountComponent:function(t){if(this._renderedComponent){var e=this._instance;if(e.componentWillUnmount&&!e._calledComponentWillUnmount)if(e._calledComponentWillUnmount=!0,t){var n=this.getName()+\".componentWillUnmount()\";f.invokeGuardedCallback(n,e.componentWillUnmount.bind(e))}else e.componentWillUnmount();this._renderedComponent&&(d.unmountComponent(this._renderedComponent,t),this._renderedNodeType=null,this._renderedComponent=null,this._instance=null),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._pendingCallbacks=null,this._pendingElement=null,this._context=null,this._rootNodeID=0,this._topLevelWrapper=null,p.remove(e)}},_maskContext:function(t){var e=this._currentElement.type,n=e.contextTypes;if(!n)return v;var r={};for(var i in n)r[i]=t[i];return r},_processContext:function(t){var e=this._maskContext(t);return e},_processChildContext:function(t){var e,n=this._currentElement.type,r=this._instance;if(r.getChildContext&&(e=r.getChildContext()),e){\"object\"!=typeof n.childContextTypes&&a(\"107\",this.getName()||\"ReactCompositeComponent\");for(var i in e)i in n.childContextTypes||a(\"108\",this.getName()||\"ReactCompositeComponent\",i);return u({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?d.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o&&a(\"136\",this.getName()||\"ReactCompositeComponent\");var u,c=!1;this._context===i?u=o.context:(u=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,u);var f=this._processPendingState(l,u),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,u):this._compositeType===y.PureClass&&(p=!g(s,l)||!g(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,u,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=u)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=u({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var c=r[a];u(o,\"function\"==typeof c?c.call(n,o,t,e):c)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(m(r,i))d.receiveComponent(n,i,t,this._processChildContext(e));else{var a=d.getHostNode(n);d.unmountComponent(n,!1);var u=h.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){s.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t=this._instance;return t.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==y.StatelessFunctional){l.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{l.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||!1===t||c.isValidElement(t)||a(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n&&a(\"110\");var r=e.getPublicInstance();(n.refs===v?n.refs={}:n.refs)[t]=r},detachRef:function(t){delete this.getPublicInstance().refs[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===y.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=b},function(t,e,n){\"use strict\";var r=n(4),i=n(372),o=n(167),a=n(24),u=n(12),c=n(385),s=n(401),l=n(171),f=n(408);n(2);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&($[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML)&&g(\"137\",t._tag,t._currentElement._owner?\" Check the render method of 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Y)Y.hasOwnProperty(n)&&t._wrapperState.listeners.push(M.trapBubbledEvent(n,Y[n],e));break;case\"source\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e),M.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topReset\",\"reset\",e),M.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function p(){P.postUpdateWrapper(this)}function h(t){Z.call(Q,t)||(X.test(t)||g(\"65\",t),Q[t]=!0)}function d(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function v(t){var e=t.type;h(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var g=n(1),m=n(3),y=n(346),_=n(348),b=n(20),x=n(83),w=n(21),C=n(160),k=n(22),E=n(84),M=n(53),T=n(161),S=n(4),N=n(365),A=n(366),P=n(162),O=n(369),I=(n(9),n(378)),D=n(383),R=(n(11),n(56)),L=(n(0),n(95),n(81),n(173)),U=(n(97),n(2),T),F=k.deleteListener,j=S.getNodeFromInstance,B=M.listenTo,V=E.registrationNameModules,W={string:!0,number:!0},z=\"__html\",H={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},q=11,Y={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},K={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},G={listing:!0,pre:!0,textarea:!0},$=m({menuitem:!0},K),X=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,Q={},Z={}.hasOwnProperty,J=1;v.displayName=\"ReactDOMComponent\",v.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=J++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(f,this);break;case\"input\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this);break;case\"option\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o);break;case\"select\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o),t.getReactMountReady().enqueue(f,this);break;case\"textarea\":O.mountWrapper(this,o,e),o=O.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this)}i(this,o);var a,p;null!=e?(a=e._namespaceURI,p=e._tag):n._tag&&(a=n._namespaceURI,p=n._tag),(null==a||a===x.svg&&\"foreignobject\"===p)&&(a=x.html),a===x.html&&(\"svg\"===this._tag?a=x.svg:\"math\"===this._tag&&(a=x.mathml)),this._namespaceURI=a;var h;if(t.useCreateElement){var d,v=n._ownerDocument;if(a===x.html)if(\"script\"===this._tag){var g=v.createElement(\"div\"),m=this._currentElement.type;g.innerHTML=\"<\"+m+\"></\"+m+\">\",d=g.removeChild(g.firstChild)}else d=o.is?v.createElement(this._currentElement.type,o.is):v.createElement(this._currentElement.type);else d=v.createElementNS(a,this._currentElement.type);S.precacheNode(this,d),this._flags|=U.hasCachedChildNodes,this._hostParent||C.setAttributeForRoot(d),this._updateDOMProperties(null,o,t);var _=b(d);this._createInitialChildren(t,o,r,_),h=_}else{var w=this._createOpenTagMarkupAndPutListeners(t,o),k=this._createContentMarkup(t,o,r);h=!k&&K[this._tag]?w+\"/>\":w+\">\"+k+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"select\":case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return h},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(V.hasOwnProperty(r))i&&o(this,r,i,t);else{\"style\"===r&&(i&&(i=this._previousStyleCopy=m({},e.style)),i=_.createMarkupForStyles(i,this));var a=null;null!=this._tag&&d(this._tag,e)?H.hasOwnProperty(r)||(a=C.createMarkupForCustomAttribute(r,i)):a=C.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+C.createMarkupForRoot()),n+=\" \"+C.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=R(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return G[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&b.queueHTML(r,i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&b.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)b.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"option\":o=A.getHostProps(this,o),a=A.getHostProps(this,a);break;case\"select\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"textarea\":o=O.getHostProps(this,o),a=O.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":N.updateWrapper(this),L.updateValueIfChanged(this);break;case\"textarea\":O.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(p,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(\"style\"===r){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else V.hasOwnProperty(r)?t[r]&&F(this,r):d(this._tag,t)?H.hasOwnProperty(r)||C.deleteValueForAttribute(j(this),r):(w.properties[r]||w.isCustomAttribute(r))&&C.deleteValueForProperty(j(this),r);for(r in e){var c=e[r],s=\"style\"===r?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(\"style\"===r)if(c?c=this._previousStyleCopy=m({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(V.hasOwnProperty(r))c?o(this,r,c,n):s&&F(this,r);else if(d(this._tag,e))H.hasOwnProperty(r)||C.setValueForAttribute(j(this),r,c);else if(w.properties[r]||w.isCustomAttribute(r)){var l=j(this);null!=c?C.setValueForProperty(l,r,c):C.deleteValueForProperty(l,r)}}a&&_.setValueForStyles(j(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=W[typeof t.children]?t.children:null,o=W[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return j(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"input\":case\"textarea\":L.stopTracking(this);break;case\"html\":case\"head\":case\"body\":g(\"66\",this._tag)}this.unmountChildren(t),S.uncacheNode(this),k.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return j(this)}},m(v.prototype,v.Mixin,I.Mixin),t.exports=v},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(97),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" 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c=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<c.length;p++){var h=c[p];if(h!==o&&h.form===o.form){var d=l.getInstanceFromNode(h);d||a(\"90\"),f.asap(r,d)}}}return n}var a=n(1),u=n(3),c=n(160),s=n(86),l=n(4),f=n(12),p=(n(0),n(2),{getHostProps:function(t,e){var n=s.getValue(e),r=s.getChecked(e);return u({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:o.bind(t),controlled:i(e)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&c.setValueForProperty(l.getNodeFromInstance(t),\"checked\",n||!1);var r=l.getNodeFromInstance(t),i=s.getValue(e);if(null!=i)if(0===i&&\"\"===r.value)r.value=\"0\";else if(\"number\"===e.type){var o=parseFloat(r.value,10)||0;(i!=o||i==o&&r.value!=i)&&(r.value=\"\"+i)}else r.value!==\"\"+i&&(r.value=\"\"+i);else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=l.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=p},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(162),c=(n(2),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){a.getNodeFromInstance(t).setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length;return{start:o,end:o+r}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(405),l=n(172),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(1),i=n(3),o=n(82),a=n(20),u=n(4),c=n(56),s=(n(0),n(97),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var s=n._ownerDocument,l=s.createComment(o),f=s.createComment(\" /react-text \"),p=a(s.createDocumentFragment());return a.queueChild(p,a(l)),this._stringText&&a.queueChild(p,a(s.createTextNode(this._stringText))),a.queueChild(p,a(f)),u.precacheNode(this,l),this._closingComment=f,p}var h=c(this._stringText);return t.renderToStaticMarkup?h:\"\\x3c!--\"+o+\"--\\x3e\"+h+\"\\x3c!-- /react-text --\\x3e\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n&&r(\"67\",this._domID),8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(1),a=n(3),u=n(86),c=n(4),s=n(12),l=(n(0),n(2),{getHostProps:function(t,e){return null!=e.dangerouslySetInnerHTML&&o(\"91\"),a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a&&o(\"92\"),Array.isArray(c)&&(c.length<=1||o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t||c(\"33\"),\"_hostNode\"in e||c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in 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i=n(345),o=n(347),a=n(349),u=n(351),c=n(352),s=n(355),l=n(357),f=n(360),p=n(4),h=n(362),d=n(370),v=n(368),g=n(371),m=n(375),y=n(376),_=n(381),b=n(386),x=n(387),w=n(388),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof Symbol&&Symbol.for&&Symbol.for(\"react.element\")||60103;t.exports=r},function(t,e,n){\"use strict\";function r(t){i.enqueueEvents(t),i.processEventQueue(!1)}var i=n(22),o={handleTopLevel:function(t,e,n,o){r(i.extractEvents(t,e,n,o))}};t.exports=o},function(t,e,n){\"use strict\";function r(t){for(;t._hostParent;)t=t._hostParent;var e=f.getNodeFromInstance(t),n=e.parentNode;return f.getClosestInstanceFromNode(n)}function i(t,e){this.topLevelType=t,this.nativeEvent=e,this.ancestors=[]}function o(t){var e=h(t.nativeEvent),n=f.getClosestInstanceFromNode(e),i=n;do{t.ancestors.push(i),i=i&&r(i)}while(i);for(var o=0;o<t.ancestors.length;o++)n=t.ancestors[o],v._handleTopLevel(t.topLevelType,n,t.nativeEvent,h(t.nativeEvent))}function 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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.01 s\n"
     ]
    }
   ],
   "source": [
    "import shap\n",
    "shap.initjs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## make datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 11.1 ms\n"
     ]
    }
   ],
   "source": [
    "corr_mat = pd.read_pickle('../data/processed/113/1/corr_mat.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/takuya.akiyama/.pyenv/versions/anaconda3-2018.12/envs/kaggle1/lib/python3.6/site-packages/ipykernel/__main__.py:7: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 31.9 s\n"
     ]
    }
   ],
   "source": [
    "feature_set = [[110, 1], [111, 1], [112, 1]]\n",
    "\n",
    "X_train = pd.concat([pd.read_pickle(f'../data/processed/{_feature_num}/{_version}/train.pickle') for _feature_num, _version in feature_set], axis=1)\n",
    "y_train = pd.read_pickle('../data/processed/000/train.pickle').set_index('ID_code').loc[X_train.index, 'target']\n",
    "\n",
    "real_id = pd.read_pickle('../data/processed/001/real_id.pickle')\n",
    "X_test = pd.concat([pd.read_pickle(f'../data/processed/{_feature_num}/{_version}/test.pickle') for _feature_num, _version in feature_set], axis=1)[X_train.columns]\n",
    "X_test = X_test.loc[real_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200000, 1000)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 3.71 ms\n"
     ]
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 1000)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 3.55 ms\n"
     ]
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## train and predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.56 ms\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cnum:0\tsimilar col:['var_0', 'var_110', 'var_44', 'var_174', 'var_76']\n",
      "\tauc : 0.547434\n",
      "\tauc : 0.547434\n",
      "cnum:1\tsimilar col:['var_1', 'var_78', 'var_43', 'var_145', 'var_109']\n",
      "\tauc : 0.545408\n",
      "\tauc : 0.573603\n",
      "cnum:2\tsimilar col:['var_2', 'var_191', 'var_0', 'var_163', 'var_44']\n",
      "\tauc : 0.552557\n",
      "\tauc : 0.596959\n",
      "cnum:3\tsimilar col:['var_3', 'var_154', 'var_83', 'var_75', 'var_55']\n",
      "\tauc : 0.509195\n",
      "\tauc : 0.598310\n",
      "cnum:4\tsimilar col:['var_4', 'var_173', 'var_35', 'var_188', 'var_156']\n",
      "\tauc : 0.508491\n",
      "\tauc : 0.598765\n",
      "cnum:5\tsimilar col:['var_5', 'var_34', 'var_109', 'var_145', 'var_1']\n",
      "\tauc : 0.530427\n",
      "\tauc : 0.608224\n",
      "cnum:6\tsimilar col:['var_6', 'var_157', 'var_106', 'var_111', 'var_147']\n",
      "\tauc : 0.559686\n",
      "\tauc : 0.627853\n",
      "cnum:7\tsimilar col:['var_7', 'var_126', 'var_134', 'var_37', 'var_101']\n",
      "\tauc : 0.498367\n",
      "\tauc : 0.627376\n",
      "cnum:8\tsimilar col:['var_8', 'var_77', 'var_20', 'var_87', 'var_104']\n",
      "\tauc : 0.517787\n",
      "\tauc : 0.629602\n",
      "cnum:9\tsimilar col:['var_9', 'var_113', 'var_75', 'var_83', 'var_55']\n",
      "\tauc : 0.540846\n",
      "\tauc : 0.640013\n",
      "cnum:10\tsimilar col:['var_10', 'var_103', 'var_158', 'var_129', 'var_14']\n",
      "\tauc : 0.497837\n",
      "\tauc : 0.639842\n",
      "cnum:11\tsimilar col:['var_11', 'var_155', 'var_26', 'var_193', 'var_6']\n",
      "\tauc : 0.520100\n",
      "\tauc : 0.642617\n",
      "cnum:12\tsimilar col:['var_12', 'var_99', 'var_89', 'var_75', 'var_95']\n",
      "\tauc : 0.560458\n",
      "\tauc : 0.659551\n",
      "cnum:13\tsimilar col:['var_13', 'var_91', 'var_132', 'var_147', 'var_184']\n",
      "\tauc : 0.552536\n",
      "\tauc : 0.670739\n",
      "cnum:14\tsimilar col:['var_14', 'var_20', 'var_8', 'var_61', 'var_77']\n",
      "\tauc : 0.506593\n",
      "\tauc : 0.670967\n",
      "cnum:15\tsimilar col:['var_15', 'var_1', 'var_145', 'var_78', 'var_43']\n",
      "\tauc : 0.514904\n",
      "\tauc : 0.671601\n",
      "cnum:16\tsimilar col:['var_16', 'var_162', 'var_123', 'var_86', 'var_157']\n",
      "\tauc : 0.503064\n",
      "\tauc : 0.671466\n",
      "cnum:17\tsimilar col:['var_17', 'var_42', 'var_7', 'var_103', 'var_112']\n",
      "\tauc : 0.499572\n",
      "\tauc : 0.671520\n",
      "cnum:18\tsimilar col:['var_18', 'var_119', 'var_114', 'var_130', 'var_49']\n",
      "\tauc : 0.537637\n",
      "\tauc : 0.678650\n",
      "cnum:19\tsimilar col:['var_19', 'var_135', 'var_166', 'var_94', 'var_81']\n",
      "\tauc : 0.510133\n",
      "\tauc : 0.679488\n",
      "cnum:20\tsimilar col:['var_20', 'var_8', 'var_150', 'var_77', 'var_177']\n",
      "\tauc : 0.519375\n",
      "\tauc : 0.681489\n",
      "cnum:21\tsimilar col:['var_21', 'var_36', 'var_172', 'var_67', 'var_190']\n",
      "\tauc : 0.557762\n",
      "\tauc : 0.692110\n",
      "cnum:22\tsimilar col:['var_22', 'var_125', 'var_56', 'var_170', 'var_133']\n",
      "\tauc : 0.552305\n",
      "\tauc : 0.701508\n",
      "cnum:23\tsimilar col:['var_23', 'var_118', 'var_35', 'var_169', 'var_107']\n",
      "\tauc : 0.521798\n",
      "\tauc : 0.702798\n",
      "cnum:24\tsimilar col:['var_24', 'var_34', 'var_127', 'var_5', 'var_1']\n",
      "\tauc : 0.528290\n",
      "\tauc : 0.704945\n",
      "cnum:25\tsimilar col:['var_25', 'var_11', 'var_62', 'var_195', 'var_197']\n",
      "\tauc : 0.508759\n",
      "\tauc : 0.705031\n",
      "cnum:26\tsimilar col:['var_26', 'var_155', 'var_86', 'var_123', 'var_0']\n",
      "\tauc : 0.557949\n",
      "\tauc : 0.715825\n",
      "cnum:27\tsimilar col:['var_27', 'var_136', 'var_120', 'var_160', 'var_126']\n",
      "\tauc : 0.499701\n",
      "\tauc : 0.715813\n",
      "cnum:28\tsimilar col:['var_28', 'var_36', 'var_148', 'var_21', 'var_165']\n",
      "\tauc : 0.519466\n",
      "\tauc : 0.716817\n",
      "cnum:29\tsimilar col:['var_29', 'var_58', 'var_71', 'var_138', 'var_130']\n",
      "\tauc : 0.502447\n",
      "\tauc : 0.716629\n",
      "cnum:30\tsimilar col:['var_30', 'var_7', 'var_41', 'var_161', 'var_17']\n",
      "\tauc : 0.499121\n",
      "\tauc : 0.716568\n",
      "cnum:31\tsimilar col:['var_31', 'var_192', 'var_104', 'var_186', 'var_140']\n",
      "\tauc : 0.525967\n",
      "\tauc : 0.717723\n",
      "cnum:32\tsimilar col:['var_32', 'var_33', 'var_53', 'var_121', 'var_40']\n",
      "\tauc : 0.526701\n",
      "\tauc : 0.720855\n",
      "cnum:33\tsimilar col:['var_33', 'var_53', 'var_40', 'var_32', 'var_180']\n",
      "\tauc : 0.539596\n",
      "\tauc : 0.726609\n",
      "cnum:34\tsimilar col:['var_34', 'var_5', 'var_24', 'var_78', 'var_145']\n",
      "\tauc : 0.544695\n",
      "\tauc : 0.731806\n",
      "cnum:35\tsimilar col:['var_35', 'var_23', 'var_118', 'var_52', 'var_107']\n",
      "\tauc : 0.534889\n",
      "\tauc : 0.734835\n",
      "cnum:36\tsimilar col:['var_36', 'var_21', 'var_118', 'var_169', 'var_28']\n",
      "\tauc : 0.537601\n",
      "\tauc : 0.737990\n",
      "cnum:37\tsimilar col:['var_37', 'var_53', 'var_180', 'var_40', 'var_109']\n",
      "\tauc : 0.505198\n",
      "\tauc : 0.737870\n",
      "cnum:38\tsimilar col:['var_38', 'var_46', 'var_98', 'var_124', 'var_144']\n",
      "\tauc : 0.498562\n",
      "\tauc : 0.737580\n",
      "cnum:39\tsimilar col:['var_39', 'var_49', 'var_141', 'var_9', 'var_79']\n",
      "\tauc : 0.504373\n",
      "\tauc : 0.737422\n",
      "cnum:40\tsimilar col:['var_40', 'var_53', 'var_88', 'var_180', 'var_33']\n",
      "\tauc : 0.546243\n",
      "\tauc : 0.743371\n",
      "cnum:41\tsimilar col:['var_41', 'var_100', 'var_46', 'var_136', 'var_185']\n",
      "\tauc : 0.501821\n",
      "\tauc : 0.743342\n",
      "cnum:42\tsimilar col:['var_42', 'var_72', 'var_121', 'var_107', 'var_164']\n",
      "\tauc : 0.506671\n",
      "\tauc : 0.743218\n",
      "cnum:43\tsimilar col:['var_43', 'var_1', 'var_78', 'var_109', 'var_165']\n",
      "\tauc : 0.524549\n",
      "\tauc : 0.744583\n",
      "cnum:44\tsimilar col:['var_44', 'var_110', 'var_0', 'var_26', 'var_2']\n",
      "\tauc : 0.543492\n",
      "\tauc : 0.751496\n",
      "cnum:45\tsimilar col:['var_45', 'var_118', 'var_23', 'var_48', 'var_169']\n",
      "\tauc : 0.519202\n",
      "\tauc : 0.752756\n",
      "cnum:46\tsimilar col:['var_46', 'var_38', 'var_41', 'var_117', 'var_124']\n",
      "\tauc : 0.502652\n",
      "\tauc : 0.752775\n",
      "cnum:47\tsimilar col:['var_47', 'var_70', 'var_85', 'var_90', 'var_84']\n",
      "\tauc : 0.507048\n",
      "\tauc : 0.752826\n",
      "cnum:48\tsimilar col:['var_48', 'var_118', 'var_36', 'var_23', 'var_28']\n",
      "\tauc : 0.533468\n",
      "\tauc : 0.755088\n",
      "cnum:49\tsimilar col:['var_49', 'var_131', 'var_151', 'var_18', 'var_9']\n",
      "\tauc : 0.527923\n",
      "\tauc : 0.757719\n",
      "cnum:50\tsimilar col:['var_50', 'var_148', 'var_165', 'var_21', 'var_88']\n",
      "\tauc : 0.513556\n",
      "\tauc : 0.757781\n",
      "cnum:51\tsimilar col:['var_51', 'var_184', 'var_102', 'var_13', 'var_91']\n",
      "\tauc : 0.523898\n",
      "\tauc : 0.759943\n",
      "cnum:52\tsimilar col:['var_52', 'var_35', 'var_118', 'var_172', 'var_23']\n",
      "\tauc : 0.527898\n",
      "\tauc : 0.761891\n",
      "cnum:53\tsimilar col:['var_53', 'var_40', 'var_88', 'var_33', 'var_180']\n",
      "\tauc : 0.554548\n",
      "\tauc : 0.768695\n",
      "cnum:54\tsimilar col:['var_54', 'var_72', 'var_121', 'var_107', 'var_42']\n",
      "\tauc : 0.518007\n",
      "\tauc : 0.769138\n",
      "cnum:55\tsimilar col:['var_55', 'var_83', 'var_9', 'var_113', 'var_79']\n",
      "\tauc : 0.512752\n",
      "\tauc : 0.769930\n",
      "cnum:56\tsimilar col:['var_56', 'var_22', 'var_125', 'var_63', 'var_170']\n",
      "\tauc : 0.526361\n",
      "\tauc : 0.772179\n",
      "cnum:57\tsimilar col:['var_57', 'var_159', 'var_169', 'var_140', 'var_149']\n",
      "\tauc : 0.514442\n",
      "\tauc : 0.772424\n",
      "cnum:58\tsimilar col:['var_58', 'var_50', 'var_148', 'var_165', 'var_28']\n",
      "\tauc : 0.525167\n",
      "\tauc : 0.773580\n",
      "cnum:59\tsimilar col:['var_59', 'var_121', 'var_92', 'var_107', 'var_32']\n",
      "\tauc : 0.509767\n",
      "\tauc : 0.773533\n",
      "cnum:60\tsimilar col:['var_60', 'var_80', 'var_174', 'var_76', 'var_155']\n",
      "\tauc : 0.506498\n",
      "\tauc : 0.773478\n",
      "cnum:61\tsimilar col:['var_61', 'var_84', 'var_118', 'var_23', 'var_128']\n",
      "\tauc : 0.503659\n",
      "\tauc : 0.773562\n",
      "cnum:62\tsimilar col:['var_62', 'var_11', 'var_44', 'var_25', 'var_0']\n",
      "\tauc : 0.511856\n",
      "\tauc : 0.773772\n",
      "cnum:63\tsimilar col:['var_63', 'var_56', 'var_22', 'var_2', 'var_125']\n",
      "\tauc : 0.511073\n",
      "\tauc : 0.774428\n",
      "cnum:64\tsimilar col:['var_64', 'var_175', 'var_13', 'var_114', 'var_130']\n",
      "\tauc : 0.511758\n",
      "\tauc : 0.774747\n",
      "cnum:65\tsimilar col:['var_65', 'var_172', 'var_67', 'var_149', 'var_23']\n",
      "\tauc : 0.509336\n",
      "\tauc : 0.774767\n",
      "cnum:66\tsimilar col:['var_66', 'var_115', 'var_6', 'var_197', 'var_139']\n",
      "\tauc : 0.518558\n",
      "\tauc : 0.775489\n",
      "cnum:67\tsimilar col:['var_67', 'var_172', 'var_149', 'var_21', 'var_36']\n",
      "\tauc : 0.543424\n",
      "\tauc : 0.778616\n",
      "cnum:68\tsimilar col:['var_68', 'var_95', 'var_49', 'var_75', 'var_151']\n",
      "\tauc : 0.508308\n",
      "\tauc : 0.778888\n",
      "cnum:69\tsimilar col:['var_69', 'var_119', 'var_13', 'var_175', 'var_132']\n",
      "\tauc : 0.507775\n",
      "\tauc : 0.779275\n",
      "cnum:70\tsimilar col:['var_70', 'var_90', 'var_121', 'var_85', 'var_107']\n",
      "\tauc : 0.525098\n",
      "\tauc : 0.781002\n",
      "cnum:71\tsimilar col:['var_71', 'var_12', 'var_75', 'var_122', 'var_89']\n",
      "\tauc : 0.529908\n",
      "\tauc : 0.782225\n",
      "cnum:72\tsimilar col:['var_72', 'var_107', 'var_121', 'var_173', 'var_178']\n",
      "\tauc : 0.512113\n",
      "\tauc : 0.782455\n",
      "cnum:73\tsimilar col:['var_73', 'var_144', 'var_121', 'var_85', 'var_32']\n",
      "\tauc : 0.503644\n",
      "\tauc : 0.782545\n",
      "cnum:74\tsimilar col:['var_74', 'var_104', 'var_87', 'var_186', 'var_8']\n",
      "\tauc : 0.519953\n",
      "\tauc : 0.783336\n",
      "cnum:75\tsimilar col:['var_75', 'var_83', 'var_12', 'var_9', 'var_154']\n",
      "\tauc : 0.537428\n",
      "\tauc : 0.786334\n",
      "cnum:76\tsimilar col:['var_76', 'var_80', 'var_174', 'var_0', 'var_155']\n",
      "\tauc : 0.559991\n",
      "\tauc : 0.792098\n",
      "cnum:77\tsimilar col:['var_77', 'var_8', 'var_20', 'var_199', 'var_112']\n",
      "\tauc : 0.512104\n",
      "\tauc : 0.792572\n",
      "cnum:78\tsimilar col:['var_78', 'var_109', 'var_1', 'var_145', 'var_43']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tauc : 0.542756\n",
      "\tauc : 0.796640\n",
      "cnum:79\tsimilar col:['var_79', 'var_9', 'var_55', 'var_113', 'var_83']\n",
      "\tauc : 0.503869\n",
      "\tauc : 0.796580\n",
      "cnum:80\tsimilar col:['var_80', 'var_76', 'var_174', 'var_155', 'var_0']\n",
      "\tauc : 0.558343\n",
      "\tauc : 0.802419\n",
      "cnum:81\tsimilar col:['var_81', 'var_148', 'var_190', 'var_122', 'var_135']\n",
      "\tauc : 0.572968\n",
      "\tauc : 0.810839\n",
      "cnum:82\tsimilar col:['var_82', 'var_187', 'var_188', 'var_90', 'var_128']\n",
      "\tauc : 0.521038\n",
      "\tauc : 0.812288\n",
      "cnum:83\tsimilar col:['var_83', 'var_75', 'var_9', 'var_55', 'var_113']\n",
      "\tauc : 0.519845\n",
      "\tauc : 0.813298\n",
      "cnum:84\tsimilar col:['var_84', 'var_48', 'var_118', 'var_116', 'var_23']\n",
      "\tauc : 0.510232\n",
      "\tauc : 0.813471\n",
      "cnum:85\tsimilar col:['var_85', 'var_194', 'var_90', 'var_121', 'var_92']\n",
      "\tauc : 0.520889\n",
      "\tauc : 0.814472\n",
      "cnum:86\tsimilar col:['var_86', 'var_198', 'var_162', 'var_26', 'var_157']\n",
      "\tauc : 0.538828\n",
      "\tauc : 0.817420\n",
      "cnum:87\tsimilar col:['var_87', 'var_173', 'var_156', 'var_178', 'var_52']\n",
      "\tauc : 0.536235\n",
      "\tauc : 0.818881\n",
      "cnum:88\tsimilar col:['var_88', 'var_40', 'var_53', 'var_180', 'var_165']\n",
      "\tauc : 0.520052\n",
      "\tauc : 0.819246\n",
      "cnum:89\tsimilar col:['var_89', 'var_99', 'var_190', 'var_12', 'var_139']\n",
      "\tauc : 0.533797\n",
      "\tauc : 0.820930\n",
      "cnum:90\tsimilar col:['var_90', 'var_85', 'var_70', 'var_121', 'var_92']\n",
      "\tauc : 0.527887\n",
      "\tauc : 0.822182\n",
      "cnum:91\tsimilar col:['var_91', 'var_13', 'var_147', 'var_184', 'var_132']\n",
      "\tauc : 0.534187\n",
      "\tauc : 0.823924\n",
      "cnum:92\tsimilar col:['var_92', 'var_121', 'var_188', 'var_90', 'var_107']\n",
      "\tauc : 0.542937\n",
      "\tauc : 0.827019\n",
      "cnum:93\tsimilar col:['var_93', 'var_179', 'var_146', 'var_133', 'var_170']\n",
      "\tauc : 0.527345\n",
      "\tauc : 0.828120\n",
      "cnum:94\tsimilar col:['var_94', 'var_166', 'var_141', 'var_122', 'var_5']\n",
      "\tauc : 0.543317\n",
      "\tauc : 0.830773\n",
      "cnum:95\tsimilar col:['var_95', 'var_12', 'var_99', 'var_139', 'var_89']\n",
      "\tauc : 0.533822\n",
      "\tauc : 0.832098\n",
      "cnum:96\tsimilar col:['var_96', 'var_83', 'var_141', 'var_154', 'var_75']\n",
      "\tauc : 0.500128\n",
      "\tauc : 0.831951\n",
      "cnum:97\tsimilar col:['var_97', 'var_33', 'var_32', 'var_53', 'var_127']\n",
      "\tauc : 0.514682\n",
      "\tauc : 0.832625\n",
      "cnum:98\tsimilar col:['var_98', 'var_167', 'var_28', 'var_144', 'var_16']\n",
      "\tauc : 0.504853\n",
      "\tauc : 0.832531\n",
      "cnum:99\tsimilar col:['var_99', 'var_12', 'var_89', 'var_190', 'var_75']\n",
      "\tauc : 0.552609\n",
      "\tauc : 0.836203\n",
      "cnum:100\tsimilar col:['var_100', 'var_41', 'var_60', 'var_102', 'var_63']\n",
      "\tauc : 0.502030\n",
      "\tauc : 0.836135\n",
      "cnum:101\tsimilar col:['var_101', 'var_32', 'var_40', 'var_134', 'var_33']\n",
      "\tauc : 0.511148\n",
      "\tauc : 0.836023\n",
      "cnum:102\tsimilar col:['var_102', 'var_51', 'var_184', 'var_91', 'var_13']\n",
      "\tauc : 0.516675\n",
      "\tauc : 0.836709\n",
      "cnum:103\tsimilar col:['var_103', 'var_10', 'var_158', 'var_129', 'var_61']\n",
      "\tauc : 0.500157\n",
      "\tauc : 0.836576\n",
      "cnum:104\tsimilar col:['var_104', 'var_74', 'var_186', 'var_31', 'var_192']\n",
      "\tauc : 0.525248\n",
      "\tauc : 0.837091\n",
      "cnum:105\tsimilar col:['var_105', 'var_44', 'var_110', 'var_2', 'var_0']\n",
      "\tauc : 0.518325\n",
      "\tauc : 0.837476\n",
      "cnum:106\tsimilar col:['var_106', 'var_6', 'var_147', 'var_157', 'var_115']\n",
      "\tauc : 0.530518\n",
      "\tauc : 0.838661\n",
      "cnum:107\tsimilar col:['var_107', 'var_121', 'var_92', 'var_178', 'var_35']\n",
      "\tauc : 0.537578\n",
      "\tauc : 0.840667\n",
      "cnum:108\tsimilar col:['var_108', 'var_162', 'var_198', 'var_111', 'var_86']\n",
      "\tauc : 0.545329\n",
      "\tauc : 0.843388\n",
      "cnum:109\tsimilar col:['var_109', 'var_78', 'var_1', 'var_43', 'var_165']\n",
      "\tauc : 0.542303\n",
      "\tauc : 0.846653\n",
      "cnum:110\tsimilar col:['var_110', 'var_0', 'var_44', 'var_26', 'var_76']\n",
      "\tauc : 0.558751\n",
      "\tauc : 0.850652\n",
      "cnum:111\tsimilar col:['var_111', 'var_157', 'var_6', 'var_162', 'var_91']\n",
      "\tauc : 0.524029\n",
      "\tauc : 0.851356\n",
      "cnum:112\tsimilar col:['var_112', 'var_199', 'var_77', 'var_8', 'var_104']\n",
      "\tauc : 0.528502\n",
      "\tauc : 0.852068\n",
      "cnum:113\tsimilar col:['var_113', 'var_9', 'var_75', 'var_141', 'var_83']\n",
      "\tauc : 0.511759\n",
      "\tauc : 0.852458\n",
      "cnum:114\tsimilar col:['var_114', 'var_18', 'var_119', 'var_130', 'var_131']\n",
      "\tauc : 0.522473\n",
      "\tauc : 0.853263\n",
      "cnum:115\tsimilar col:['var_115', 'var_106', 'var_6', 'var_66', 'var_175']\n",
      "\tauc : 0.543992\n",
      "\tauc : 0.855072\n",
      "cnum:116\tsimilar col:['var_116', 'var_127', 'var_145', 'var_1', 'var_34']\n",
      "\tauc : 0.522848\n",
      "\tauc : 0.855448\n",
      "cnum:117\tsimilar col:['var_117', 'var_69', 'var_96', 'var_3', 'var_151']\n",
      "\tauc : 0.505411\n",
      "\tauc : 0.855440\n",
      "cnum:118\tsimilar col:['var_118', 'var_23', 'var_169', 'var_35', 'var_48']\n",
      "\tauc : 0.541649\n",
      "\tauc : 0.857025\n",
      "cnum:119\tsimilar col:['var_119', 'var_18', 'var_114', 'var_130', 'var_49']\n",
      "\tauc : 0.527584\n",
      "\tauc : 0.858304\n",
      "cnum:120\tsimilar col:['var_120', 'var_74', 'var_186', 'var_104', 'var_31']\n",
      "\tauc : 0.513645\n",
      "\tauc : 0.858510\n",
      "cnum:121\tsimilar col:['var_121', 'var_92', 'var_107', 'var_90', 'var_70']\n",
      "\tauc : 0.535406\n",
      "\tauc : 0.860285\n",
      "cnum:122\tsimilar col:['var_122', 'var_154', 'var_166', 'var_99', 'var_12']\n",
      "\tauc : 0.538430\n",
      "\tauc : 0.862021\n",
      "cnum:123\tsimilar col:['var_123', 'var_162', 'var_157', 'var_26', 'var_198']\n",
      "\tauc : 0.542380\n",
      "\tauc : 0.863954\n",
      "cnum:124\tsimilar col:['var_124', 'var_98', 'var_7', 'var_38', 'var_126']\n",
      "\tauc : 0.505356\n",
      "\tauc : 0.863937\n",
      "cnum:125\tsimilar col:['var_125', 'var_22', 'var_170', 'var_133', 'var_146']\n",
      "\tauc : 0.522088\n",
      "\tauc : 0.864425\n",
      "cnum:126\tsimilar col:['var_126', 'var_164', 'var_153', 'var_176', 'var_196']\n",
      "\tauc : 0.497916\n",
      "\tauc : 0.864328\n",
      "cnum:127\tsimilar col:['var_127', 'var_1', 'var_43', 'var_165', 'var_109']\n",
      "\tauc : 0.535899\n",
      "\tauc : 0.865889\n",
      "cnum:128\tsimilar col:['var_128', 'var_187', 'var_35', 'var_188', 'var_178']\n",
      "\tauc : 0.523859\n",
      "\tauc : 0.866609\n",
      "cnum:129\tsimilar col:['var_129', 'var_65', 'var_159', 'var_189', 'var_57']\n",
      "\tauc : 0.503792\n",
      "\tauc : 0.866689\n",
      "cnum:130\tsimilar col:['var_130', 'var_18', 'var_114', 'var_119', 'var_49']\n",
      "\tauc : 0.525238\n",
      "\tauc : 0.867689\n",
      "cnum:131\tsimilar col:['var_131', 'var_49', 'var_114', 'var_75', 'var_9']\n",
      "\tauc : 0.528630\n",
      "\tauc : 0.868397\n",
      "cnum:132\tsimilar col:['var_132', 'var_13', 'var_147', 'var_91', 'var_162']\n",
      "\tauc : 0.520910\n",
      "\tauc : 0.868974\n",
      "cnum:133\tsimilar col:['var_133', 'var_146', 'var_170', 'var_125', 'var_22']\n",
      "\tauc : 0.546103\n",
      "\tauc : 0.871041\n",
      "cnum:134\tsimilar col:['var_134', 'var_85', 'var_92', 'var_32', 'var_194']\n",
      "\tauc : 0.515191\n",
      "\tauc : 0.871399\n",
      "cnum:135\tsimilar col:['var_135', 'var_166', 'var_81', 'var_195', 'var_122']\n",
      "\tauc : 0.525298\n",
      "\tauc : 0.872351\n",
      "cnum:136\tsimilar col:['var_136', 'var_42', 'var_72', 'var_54', 'var_87']\n",
      "\tauc : 0.504335\n",
      "\tauc : 0.872317\n",
      "cnum:137\tsimilar col:['var_137', 'var_1', 'var_78', 'var_127', 'var_145']\n",
      "\tauc : 0.525318\n",
      "\tauc : 0.873101\n",
      "cnum:138\tsimilar col:['var_138', 'var_6', 'var_115', 'var_106', 'var_139']\n",
      "\tauc : 0.517127\n",
      "\tauc : 0.873266\n",
      "cnum:139\tsimilar col:['var_139', 'var_190', 'var_99', 'var_89', 'var_12']\n",
      "\tauc : 0.571410\n",
      "\tauc : 0.877396\n",
      "cnum:140\tsimilar col:['var_140', 'var_57', 'var_52', 'var_31', 'var_186']\n",
      "\tauc : 0.513255\n",
      "\tauc : 0.877478\n",
      "cnum:141\tsimilar col:['var_141', 'var_154', 'var_113', 'var_122', 'var_94']\n",
      "\tauc : 0.528650\n",
      "\tauc : 0.878550\n",
      "cnum:142\tsimilar col:['var_142', 'var_40', 'var_169', 'var_149', 'var_53']\n",
      "\tauc : 0.518395\n",
      "\tauc : 0.878845\n",
      "cnum:143\tsimilar col:['var_143', 'var_99', 'var_195', 'var_190', 'var_139']\n",
      "\tauc : 0.506708\n",
      "\tauc : 0.878781\n",
      "cnum:144\tsimilar col:['var_144', 'var_165', 'var_53', 'var_127', 'var_33']\n",
      "\tauc : 0.519572\n",
      "\tauc : 0.879121\n",
      "cnum:145\tsimilar col:['var_145', 'var_78', 'var_1', 'var_109', 'var_127']\n",
      "\tauc : 0.527972\n",
      "\tauc : 0.880015\n",
      "cnum:146\tsimilar col:['var_146', 'var_133', 'var_170', 'var_125', 'var_22']\n",
      "\tauc : 0.556130\n",
      "\tauc : 0.882648\n",
      "cnum:147\tsimilar col:['var_147', 'var_91', 'var_13', 'var_132', 'var_106']\n",
      "\tauc : 0.538127\n",
      "\tauc : 0.884222\n",
      "cnum:148\tsimilar col:['var_148', 'var_81', 'var_21', 'var_190', 'var_122']\n",
      "\tauc : 0.549786\n",
      "\tauc : 0.885788\n",
      "cnum:149\tsimilar col:['var_149', 'var_67', 'var_172', 'var_21', 'var_169']\n",
      "\tauc : 0.549372\n",
      "\tauc : 0.887277\n",
      "cnum:150\tsimilar col:['var_150', 'var_177', 'var_87', 'var_173', 'var_156']\n",
      "\tauc : 0.524965\n",
      "\tauc : 0.887872\n",
      "cnum:151\tsimilar col:['var_151', 'var_49', 'var_18', 'var_114', 'var_131']\n",
      "\tauc : 0.522273\n",
      "\tauc : 0.888416\n",
      "cnum:152\tsimilar col:['var_152', 'var_163', 'var_191', 'var_168', 'var_2']\n",
      "\tauc : 0.508567\n",
      "\tauc : 0.888517\n",
      "cnum:153\tsimilar col:['var_153', 'var_164', 'var_196', 'var_128', 'var_188']\n",
      "\tauc : 0.504759\n",
      "\tauc : 0.888451\n",
      "cnum:154\tsimilar col:['var_154', 'var_122', 'var_75', 'var_141', 'var_166']\n",
      "\tauc : 0.544412\n",
      "\tauc : 0.889851\n",
      "cnum:155\tsimilar col:['var_155', 'var_26', 'var_80', 'var_76', 'var_174']\n",
      "\tauc : 0.533286\n",
      "\tauc : 0.891065\n",
      "cnum:156\tsimilar col:['var_156', 'var_173', 'var_196', 'var_178', 'var_128']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tauc : 0.515694\n",
      "\tauc : 0.891313\n",
      "cnum:157\tsimilar col:['var_157', 'var_6', 'var_111', 'var_106', 'var_147']\n",
      "\tauc : 0.528775\n",
      "\tauc : 0.892198\n",
      "cnum:158\tsimilar col:['var_158', 'var_129', 'var_65', 'var_14', 'var_159']\n",
      "\tauc : 0.501853\n",
      "\tauc : 0.892198\n",
      "cnum:159\tsimilar col:['var_159', 'var_57', 'var_140', 'var_65', 'var_149']\n",
      "\tauc : 0.512591\n",
      "\tauc : 0.892299\n",
      "cnum:160\tsimilar col:['var_160', 'var_177', 'var_150', 'var_173', 'var_87']\n",
      "\tauc : 0.504927\n",
      "\tauc : 0.892332\n",
      "cnum:161\tsimilar col:['var_161', 'var_171', 'var_43', 'var_3', 'var_137']\n",
      "\tauc : 0.504964\n",
      "\tauc : 0.892297\n",
      "cnum:162\tsimilar col:['var_162', 'var_198', 'var_86', 'var_123', 'var_108']\n",
      "\tauc : 0.527965\n",
      "\tauc : 0.892963\n",
      "cnum:163\tsimilar col:['var_163', 'var_191', 'var_2', 'var_22', 'var_125']\n",
      "\tauc : 0.535279\n",
      "\tauc : 0.894010\n",
      "cnum:164\tsimilar col:['var_164', 'var_196', 'var_156', 'var_128', 'var_188']\n",
      "\tauc : 0.539578\n",
      "\tauc : 0.895439\n",
      "cnum:165\tsimilar col:['var_165', 'var_43', 'var_109', 'var_78', 'var_127']\n",
      "\tauc : 0.551495\n",
      "\tauc : 0.897448\n",
      "cnum:166\tsimilar col:['var_166', 'var_94', 'var_135', 'var_122', 'var_154']\n",
      "\tauc : 0.550856\n",
      "\tauc : 0.899182\n",
      "cnum:167\tsimilar col:['var_167', 'var_118', 'var_28', 'var_48', 'var_107']\n",
      "\tauc : 0.529256\n",
      "\tauc : 0.899828\n",
      "cnum:168\tsimilar col:['var_168', 'var_2', 'var_163', 'var_191', 'var_125']\n",
      "\tauc : 0.513202\n",
      "\tauc : 0.900053\n",
      "cnum:169\tsimilar col:['var_169', 'var_118', 'var_21', 'var_149', 'var_23']\n",
      "\tauc : 0.545523\n",
      "\tauc : 0.901274\n",
      "cnum:170\tsimilar col:['var_170', 'var_133', 'var_146', 'var_125', 'var_22']\n",
      "\tauc : 0.541929\n",
      "\tauc : 0.902615\n",
      "cnum:171\tsimilar col:['var_171', 'var_43', 'var_1', 'var_78', 'var_109']\n",
      "\tauc : 0.513682\n",
      "\tauc : 0.902793\n",
      "cnum:172\tsimilar col:['var_172', 'var_67', 'var_21', 'var_149', 'var_118']\n",
      "\tauc : 0.540583\n",
      "\tauc : 0.903916\n",
      "cnum:173\tsimilar col:['var_173', 'var_87', 'var_156', 'var_178', 'var_35']\n",
      "\tauc : 0.539535\n",
      "\tauc : 0.904912\n",
      "cnum:174\tsimilar col:['var_174', 'var_80', 'var_76', 'var_0', 'var_26']\n",
      "\tauc : 0.553786\n",
      "\tauc : 0.906917\n",
      "cnum:175\tsimilar col:['var_175', 'var_147', 'var_13', 'var_115', 'var_91']\n",
      "\tauc : 0.520943\n",
      "\tauc : 0.907292\n",
      "cnum:176\tsimilar col:['var_176', 'var_121', 'var_72', 'var_194', 'var_188']\n",
      "\tauc : 0.506789\n",
      "\tauc : 0.907316\n",
      "cnum:177\tsimilar col:['var_177', 'var_150', 'var_87', 'var_173', 'var_156']\n",
      "\tauc : 0.534759\n",
      "\tauc : 0.908446\n",
      "cnum:178\tsimilar col:['var_178', 'var_173', 'var_107', 'var_35', 'var_156']\n",
      "\tauc : 0.519732\n",
      "\tauc : 0.908662\n",
      "cnum:179\tsimilar col:['var_179', 'var_93', 'var_133', 'var_170', 'var_146']\n",
      "\tauc : 0.544085\n",
      "\tauc : 0.909942\n",
      "cnum:180\tsimilar col:['var_180', 'var_53', 'var_109', 'var_40', 'var_165']\n",
      "\tauc : 0.526087\n",
      "\tauc : 0.910661\n",
      "cnum:181\tsimilar col:['var_181', 'var_174', 'var_26', 'var_123', 'var_0']\n",
      "\tauc : 0.511429\n",
      "\tauc : 0.910597\n",
      "cnum:182\tsimilar col:['var_182', 'var_97', 'var_144', 'var_32', 'var_194']\n",
      "\tauc : 0.506271\n",
      "\tauc : 0.910664\n",
      "cnum:183\tsimilar col:['var_183', 'var_173', 'var_167', 'var_156', 'var_35']\n",
      "\tauc : 0.502130\n",
      "\tauc : 0.910615\n",
      "cnum:184\tsimilar col:['var_184', 'var_13', 'var_91', 'var_51', 'var_102']\n",
      "\tauc : 0.547157\n",
      "\tauc : 0.912035\n",
      "cnum:185\tsimilar col:['var_185', 'var_37', 'var_182', 'var_134', 'var_97']\n",
      "\tauc : 0.499519\n",
      "\tauc : 0.912033\n",
      "cnum:186\tsimilar col:['var_186', 'var_104', 'var_31', 'var_8', 'var_87']\n",
      "\tauc : 0.527887\n",
      "\tauc : 0.912352\n",
      "cnum:187\tsimilar col:['var_187', 'var_82', 'var_128', 'var_188', 'var_90']\n",
      "\tauc : 0.512955\n",
      "\tauc : 0.912755\n",
      "cnum:188\tsimilar col:['var_188', 'var_92', 'var_35', 'var_128', 'var_82']\n",
      "\tauc : 0.529775\n",
      "\tauc : 0.913596\n",
      "cnum:189\tsimilar col:['var_189', 'var_128', 'var_187', 'var_188', 'var_90']\n",
      "\tauc : 0.508967\n",
      "\tauc : 0.913593\n",
      "cnum:190\tsimilar col:['var_190', 'var_89', 'var_99', 'var_139', 'var_12']\n",
      "\tauc : 0.551635\n",
      "\tauc : 0.915085\n",
      "cnum:191\tsimilar col:['var_191', 'var_2', 'var_163', 'var_44', 'var_0']\n",
      "\tauc : 0.544378\n",
      "\tauc : 0.916221\n",
      "cnum:192\tsimilar col:['var_192', 'var_31', 'var_104', 'var_186', 'var_52']\n",
      "\tauc : 0.542899\n",
      "\tauc : 0.916921\n",
      "cnum:193\tsimilar col:['var_193', 'var_11', 'var_44', 'var_2', 'var_26']\n",
      "\tauc : 0.516065\n",
      "\tauc : 0.917075\n",
      "cnum:194\tsimilar col:['var_194', 'var_85', 'var_90', 'var_134', 'var_92']\n",
      "\tauc : 0.519345\n",
      "\tauc : 0.917241\n",
      "cnum:195\tsimilar col:['var_195', 'var_190', 'var_21', 'var_89', 'var_135']\n",
      "\tauc : 0.523830\n",
      "\tauc : 0.917636\n",
      "cnum:196\tsimilar col:['var_196', 'var_164', 'var_156', 'var_178', 'var_128']\n",
      "\tauc : 0.521091\n",
      "\tauc : 0.917893\n",
      "cnum:197\tsimilar col:['var_197', 'var_49', 'var_89', 'var_139', 'var_151']\n",
      "\tauc : 0.529217\n",
      "\tauc : 0.918419\n",
      "cnum:198\tsimilar col:['var_198', 'var_86', 'var_162', 'var_123', 'var_157']\n",
      "\tauc : 0.548357\n",
      "\tauc : 0.919910\n",
      "cnum:199\tsimilar col:['var_199', 'var_112', 'var_77', 'var_8', 'var_104']\n",
      "\tauc : 0.525319\n",
      "\tauc : 0.920245\n",
      "time: 37min 8s\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "    'bagging_freq': 5,\n",
    "    'bagging_fraction': 0.9,\n",
    "    'boost_from_average':'false',\n",
    "    'boost': 'gbdt',\n",
    "    'feature_fraction': 1.0,\n",
    "    'learning_rate': 0.05,\n",
    "    'max_depth': -1,\n",
    "    'metric':'binary_logloss',\n",
    "    'min_data_in_leaf': 10,\n",
    "    'min_sum_hessian_in_leaf': 10.0,\n",
    "    'num_leaves': 4,\n",
    "    'num_threads': 32,\n",
    "    'tree_learner': 'serial',\n",
    "    'objective': 'binary',\n",
    "    'verbosity': 1}\n",
    "\n",
    "oof_pred_array = np.ones((200000, 200))\n",
    "test_pred_array = np.ones((100000, 5, 200))\n",
    "for cnum in range(200):\n",
    "    #collist = corr_mat.loc[corr_mat[f'var_{cnum}'] > 0.98, f'var_{cnum}'].sort_values(ascending=False).index.tolist()\n",
    "    collist = corr_mat[f'var_{cnum}'].sort_values(ascending=False)[:5].index.tolist()\n",
    "    print(f'cnum:{cnum}\\tsimilar col:{collist}')\n",
    "\n",
    "    skf = StratifiedKFold(n_splits=5)\n",
    "    #folds = [[np.concatenate([_trn+i * X_train.shape[0] for i in range(len(collist))]), np.concatenate([np.array(_val) for i in range(len(collist))])] for _trn, _val in skf.split(X_train, y_train)]\n",
    "    folds = [[np.concatenate([_trn+i * X_train.shape[0] for i in range(len(collist))]), np.array(_val)] for _trn, _val in skf.split(X_train, y_train)]\n",
    "    train_dset = lgb.Dataset(\n",
    "        np.concatenate([X_train.filter(regex=f'{c}$').values for c in collist], axis=0), \n",
    "        np.concatenate([y_train for c in range(len(collist))], axis=0), free_raw_data=False, \n",
    "        weight=np.concatenate([np.ones((X_train.shape[0]))*len(collist), np.ones((X_train.shape[0]*(len(collist)-1)))])\n",
    "    )\n",
    "    cv_result, bsts = lgb.cv(params, train_set=train_dset, num_boost_round=100000, early_stopping_rounds=100, \n",
    "                             folds=folds)\n",
    "\n",
    "    best_iteration = bsts.best_iteration\n",
    "    for i, bst in enumerate(bsts.boosters):\n",
    "        # oofの予測\n",
    "        cv_valid_index = bst.valid_sets[0].used_indices\n",
    "        #cv_valid_index = cv_valid_index[:int(cv_valid_index.shape[0]/len(collist))]\n",
    "        cv_valid_data = X_train.filter(regex=f'var_{cnum}$').iloc[cv_valid_index].values\n",
    "        oof_pred_array[cv_valid_index, cnum] = bst.predict(cv_valid_data, num_iteration=best_iteration)\n",
    "        # testの予測\n",
    "        test_pred_array[:, i, cnum] = bst.predict(X_test.filter(regex=f'var_{cnum}$'), num_iteration=best_iteration)\n",
    "    print('\\tauc : {0:.6f}'.format(roc_auc_score(y_train, oof_pred_array[:, cnum])))\n",
    "    print('\\tauc : {0:.6f}'.format(roc_auc_score(y_train, oof_pred_array.prod(axis=1))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.75 ms\n"
     ]
    }
   ],
   "source": [
    "cv_index_list = []\n",
    "for i, bst in enumerate(bsts.boosters):\n",
    "    train_index = bst.train_set.used_indices\n",
    "    train_index = train_index[:int(train_index.shape[0]/len(collist))]\n",
    "    valid_index = bst.valid_sets[0].used_indices\n",
    "    cv_index_list.append([train_index, valid_index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.920244738204284"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 216 ms\n"
     ]
    }
   ],
   "source": [
    "roc_auc_score(y_train, oof_pred_array.prod(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9207698087422679"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 279 ms\n"
     ]
    }
   ],
   "source": [
    "roc_auc_score(y_train, (9 * oof_pred_array / (1 - oof_pred_array)).prod(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "threashold : 0.507\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "26e03295ee9f48aeb639c73f495b6f01",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=200), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "raw prod auc : 0.920736\n",
      "odds prod auc : 0.921179\n",
      "time: 22.1 s\n"
     ]
    }
   ],
   "source": [
    "thr = 0.507\n",
    "print(f'threashold : {thr}')\n",
    "oof_pred_prod = np.ones((200000))\n",
    "oof_pred_odds_prod = np.ones((200000))\n",
    "test_pred_odds_prod = np.ones((100000, 5))\n",
    "for cnum in tqdm(range(200)):\n",
    "    tmp_auc = roc_auc_score(y_train, oof_pred_array[:, cnum])\n",
    "    if tmp_auc >= thr:\n",
    "        oof_pred_prod *= oof_pred_array[:, cnum]\n",
    "        oof_pred_odds_prod *= 9 * oof_pred_array[:, cnum] / (1 - oof_pred_array[:, cnum])\n",
    "        test_pred_odds_prod *= 9 * test_pred_array[:,:, cnum] / (1 - test_pred_array[:,:, cnum])\n",
    "print('raw prod auc : {0:.6f}'.format(roc_auc_score(y_train, oof_pred_prod)))\n",
    "print('odds prod auc : {0:.6f}'.format(roc_auc_score(y_train, oof_pred_odds_prod)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 48.3 s\n"
     ]
    }
   ],
   "source": [
    "model_number = 910\n",
    "model_output_save_dir = f'../data/processed/{model_number}'\n",
    "if not os.path.isdir(model_output_save_dir):\n",
    "    os.makedirs(model_output_save_dir)\n",
    "\n",
    "pd.DataFrame(oof_pred_array, index=X_train.index, columns=[f'pred_{i}' for i in range(200)])\\\n",
    "    .to_pickle(os.path.join(model_output_save_dir, 'oof_preds.pkl.gz'), compression='gzip')\n",
    "pd.DataFrame(cv_index_list, columns=['train_index', 'valid_index'])\\\n",
    "    .to_pickle(os.path.join(model_output_save_dir, 'cv_index.pkl.gz'), compression='gzip')\n",
    "pd.to_pickle(test_pred_array, os.path.join(model_output_save_dir, 'test_pred_array.pickle'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 375
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 375
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 375
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 375
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 375
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 375
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 375
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 375
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 375
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 375
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 10.4 s\n"
     ]
    }
   ],
   "source": [
    "for c in range(10):\n",
    "    pd.Series(oof_pred_array[:, c]).hist(bins=np.arange(0, 1, 0.01), alpha=0.5)\n",
    "    ax = pd.Series(test_pred_array[:, 0, c]).hist(bins=np.arange(0, 1, 0.01), alpha=0.5)\n",
    "    ax.set_yscale('log')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 10.7 ms\n"
     ]
    }
   ],
   "source": [
    "oof_pred = pd.DataFrame({'pred':oof_pred_odds_prod, 'label':y_train}, index=y_train.index)\n",
    "test_pred = pd.DataFrame(test_pred_odds_prod, index=X_test.index, columns=[f'fold_{i}' for i in range(5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'test prediction')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 589,
       "width": 1156
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2.41 s\n"
     ]
    }
   ],
   "source": [
    "bins=np.arange(0, 1000, 10) #np.arange(-0.1, 1.1, 0.01)\n",
    "\n",
    "fig, axs = plt.subplots(2, 1, figsize=(20,10), sharex=True)\n",
    "ax = axs[0]\n",
    "oof_pred.query('label==0')['pred'].hist(bins = bins, ax=ax, color='b', alpha=0.5)\n",
    "oof_pred.query('label==1')['pred'].hist(bins = bins, ax=ax, color='r', alpha=0.5)\n",
    "ax.set_yscale('log')\n",
    "ax.set_title('oof prediction and label')\n",
    "ax = axs[1]\n",
    "test_pred.mean(axis=1).hist(bins = bins, ax=ax, color='b', alpha=0.5)\n",
    "ax.set_yscale('log')\n",
    "ax.set_title('test prediction')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## submit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 58.2 ms\n"
     ]
    }
   ],
   "source": [
    "sub = pd.read_pickle('../data/processed/000/sample_submission.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 349 ms\n"
     ]
    }
   ],
   "source": [
    "sub = sub[['ID_code']].merge((test_pred.rank()/test_pred.shape[0]).mean(axis=1).reset_index(name='target')\\\n",
    "                             .rename(columns={'index':'ID_code'}), how='left').fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_code</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test_0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test_1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test_2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>test_3</td>\n",
       "      <td>0.859428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>test_4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ID_code    target\n",
       "0  test_0  0.000000\n",
       "1  test_1  0.000000\n",
       "2  test_2  0.000000\n",
       "3  test_3  0.859428\n",
       "4  test_4  0.000000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 11.2 ms\n"
     ]
    }
   ],
   "source": [
    "sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.44 s\n"
     ]
    }
   ],
   "source": [
    "if not os.path.isdir('../data/out'):\n",
    "    os.makedirs('../data/out')\n",
    "sub.to_csv(f'../data/out/{model_number}_lgb.csv.gz', index=False, compression='gzip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9951170644497827"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 424 ms\n"
     ]
    }
   ],
   "source": [
    "pd.read_csv(f'../data/out/{model_number}_lgb.csv.gz').set_index('ID_code')['target'].corr(\n",
    "    pd.read_csv('../data/out/904_lgb.csv.gz').set_index('ID_code')['target'], method='spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9922740112583734"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 400 ms\n"
     ]
    }
   ],
   "source": [
    "pd.read_csv(f'../data/out/{model_number}_lgb.csv.gz').set_index('ID_code')['target'].corr(\n",
    "    pd.read_csv('../data/external/lgb_sub_hrd_0404_2.csv').set_index('ID_code')['target'], method='spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../data/out/910_lgb.csv.gz\n",
      "time: 13.2 ms\n"
     ]
    }
   ],
   "source": [
    "print(f'../data/out/{model_number}_lgb.csv.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /home/takuya.akiyama/.kaggle/kaggle.json'\n",
      "100%|████████████████████████████████████████| 960k/960k [00:00<00:00, 3.77MB/s]\n",
      "Successfully submitted to Santander Customer Transaction Predictiontime: 2.38 s\n"
     ]
    }
   ],
   "source": [
    "!kaggle competitions submit -c santander-customer-transaction-prediction -f ../data/out/910_lgb.csv.gz -m 'cv:0.921179'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 370 ms\n"
     ]
    }
   ],
   "source": [
    "sub1 = pd.read_csv('../data/out/904_lgb.csv.gz')\n",
    "sub2 = pd.read_csv('../data/out/910_lgb.csv.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9951170644497827"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 101 ms\n"
     ]
    }
   ],
   "source": [
    "sub1.set_index('ID_code')['target'].corr(sub2.set_index('ID_code')['target'], method='spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 99 ms\n"
     ]
    }
   ],
   "source": [
    "rankave_sub = ((sub1.set_index('ID_code')['target'].rank()/2 + sub2.set_index('ID_code')['target'].rank()/2)/sub1.shape[0]).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.52 s\n"
     ]
    }
   ],
   "source": [
    "rankave_sub.to_csv(f'../data/out/rankave_904_910.csv.gz', index=False, compression='gzip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9987720324215111"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 396 ms\n"
     ]
    }
   ],
   "source": [
    "pd.read_csv(f'../data/out/rankave_904_910.csv.gz').set_index('ID_code')['target'].corr(\n",
    "    pd.read_csv('../data/out/904_lgb.csv.gz').set_index('ID_code')['target'], method='spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9987719349881313"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 388 ms\n"
     ]
    }
   ],
   "source": [
    "pd.read_csv(f'../data/out/rankave_904_910.csv.gz').set_index('ID_code')['target'].corr(\n",
    "    pd.read_csv('../data/out/910_lgb.csv.gz').set_index('ID_code')['target'], method='spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /home/takuya.akiyama/.kaggle/kaggle.json'\n",
      "100%|████████████████████████████████████████| 897k/897k [00:00<00:00, 3.82MB/s]\n",
      "Successfully submitted to Santander Customer Transaction Predictiontime: 4.2 s\n"
     ]
    }
   ],
   "source": [
    "!kaggle competitions submit -c santander-customer-transaction-prediction -f ../data/out/rankave_904_910.csv.gz -m 'no comment'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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